Evaluation of Dissolved Organic Carbon Using Synchronized Fluorescence Emission Spectra and Unsupervised Method of Principal Component Analysis (PCA) and Independent Component Analysis (ICA)

Tais Cristina Filippe,Luana Mayumi Takahasi Marques,Jorge Costa Pereira,Heloise G Knapik,Júlio César Rodrigues De Azevedo

doi:10.4236/jwarp.2019.113015

Tais Cristina Filippe, Luana Mayumi Takahasi Marques + Show 3 more

Open Access

https://doi.org/10.4236/jwarp.2019.113015

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Abstract

Dissolved organic matter (DOM) can be originated from autochthonous or allochthonous sources, where allochthonous DOM can be from pedogenic sources (humic substances—HSs) or anthropogenicsources (wastewater). The analysis of fluorescence emission, excitation, synchronous or excitation-emission matrix (EEM) have been used to identify the main source or probable contribution of dissolved compounds, such as humic acids (HA), fulvic acids (FA) and dissolved organic carbon (DOC) from sewage, but does not quantify. Fluorescence emission is a powerful technique to detect and qualify organic dissolved compounds but fails in quantitative aspects. In this work, we propose an in situ method for direct determination of DOC using synchronous fluorescence spectra with independent component analysis (ICA). Well known standard solutions were used for method development and validation. In this work, we show that it is possible to predict the number of independent contributions using an unsupervised method based on iterative Principal Component Analysis and Independent Component Analysis (PCA-ICA) approach over combined matrix results. Within these results it’s also possible to see that with a very small amount of independent components it is possible to describe environmental samples of HA, FA and primary productivity (PP).

Highlights

Disordered population growth in urban and rural areas has changed the water quality in different aquatic environments
We show that it is possible to predict the number of independent contributions using an unsupervised method based on iterative Principal Component Analysis and Independent Component Analysis (PCA-independent component analysis (ICA)) approach over combined matrix results
Better results are obtained considering c = 5 and using respective parsimonious models in order to ensure a good fitting without extra deterioration of its prediction ability. From this preliminary study with simple standard solutions, some conclusions are drown: 1) It is possible to predict the number of independent contributions using a unsupervised method based on iterative PCA-ICA approach over mixing matrix results; 2) Predicted components are consistent with spectra signal recovery; 3) These estimated components are able to accurately describe dissolved organic carbon (DOC) via linear first degree polynomial models; 4) Some difficulties arrive when dealing with real mixtures—it may be present some interfering effect and a relative lack on signal linearity in respect to solute concentration; 5) Fitting results a predicting ability may be increased using quadratic polynomial models; 6) Comparing spectral deconvoluted contributions it is possible to identify bands related to each component and identify its contribution in solution

Summary

Introduction

Disordered population growth in urban and rural areas has changed the water quality in different aquatic environments (rivers, lakes, ponds, streams). Water quality in rivers and lakes is related with the physical, chemical, and biological characteristics of an aquatic environment. Human occupancy has caused abiotic changes (nutrient cycling, metals and organic matter) and biotic. To evaluate and follow these changes, it is necessary to perform a distinct monitoring strategy in aquatic environments. It becomes important to monitor the variations and origin of organic matter (OM) in aquatic environments, which can be among the main factors of biotic functioning of these ecosystems [1] [2] [3] [4]

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